International Journal of Next-Generation Computing最新文献_第2页

Modeling of Urban Heat Island using Artificial Neural Network and Remote Sensing in Tehran 利用人工神经网络和遥感技术建立德黑兰城市热岛模型

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International Journal of Next-Generation Computing Pub Date : 2023-11-28 DOI: 10.47164/ijngc.v14i4.1314

Zahra Azizi, Navid Zoghi, Saeed Behzadi

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Investigation of Available Datasets and Techniques for Visual Question Answering 可视化问题解答的可用数据集和技术调查

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International Journal of Next-Generation Computing Pub Date : 2023-08-03 DOI: 10.47164/ijngc.v14i3.767

Lata A. Bhavnani, Dr. Narendra Patel

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Deep Learning Architecture U-Net Based Road Network Detection from Remote Sensing Images 基于深度学习架构 U-Net 的遥感图像路网检测

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International Journal of Next-Generation Computing Pub Date : 2023-07-31 DOI: 10.47164/ijngc.v14i3.1301

Miral J. Patel, Hasmukh P Koringa

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Neural Network-based Dynamic Clustering Model for Energy Efficient Data Uploading and Downloading in Green Vehicular Ad-hoc Networks 基于神经网络的动态聚类模型用于绿色车载 Ad-hoc 网络中的节能数据上传和下载

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International Journal of Next-Generation Computing Pub Date : 2023-07-31 DOI: 10.47164/ijngc.v14i3.1150

Amit Choksi, Mehul Shah

{"title":"Neural Network-based Dynamic Clustering Model for Energy Efficient Data Uploading and Downloading in Green Vehicular Ad-hoc Networks","authors":"Amit Choksi, Mehul Shah","doi":"10.47164/ijngc.v14i3.1150","DOIUrl":"https://doi.org/10.47164/ijngc.v14i3.1150","url":null,"abstract":"Green VANET is an emerging field of research that spurs interest in energy consumption management for the development of smart cities. It also presents a unique variety of research problems for developing trustworthy and scalable routing protocols as vehicles are susceptible to the restrictions of road geometry and the barriers which limit networking capabilities in urban environments. Clustering is a process of assembling vehicle nodes to create a powerful and effective network infrastructure. According to recent studies, clustering-based routing algorithms in green VANET may significantly improve networking effectiveness and lower infrastructure costs. However, sometimes the vehicle nodes are unaware of their OBU energy consumption, which causes network execution issues and topological alterations. In such instances, the energy consumption of onboard sensors becomes a major problem in the clustering-based routing protocol. Hence, this paper proposes a self-organizing map neural network (SOMNN) based dynamic clustering model to identify energy-efficient nodes from each cluster for vehicular data uploading and downloading applications. The simulation results demonstrate that the proposed model solves network lifetime issues and provides superior network effectiveness with enhanced communication stability. The suggested dynamic clustering model reduces network energy consumption by 26% and 18% in comparison to k – means (KM) and fuzzy c – means (FCM) based clustering model.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"66 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139353661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Detection of Tuta absoluta larvae and their damages in tomatoes with deep learning-based algorithm 利用基于深度学习的算法检测西红柿中的 Tuta absoluta 幼虫及其危害

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International Journal of Next-Generation Computing Pub Date : 2023-07-31 DOI: 10.47164/ijngc.v14i3.1287

Yavuz Selim Şahi̇n, Atilla Erdi̇nç, Alperen Kaan Bütüner, Hilal Erdoğan

{"title":"Detection of Tuta absoluta larvae and their damages in tomatoes with deep learning-based algorithm","authors":"Yavuz Selim Şahi̇n, Atilla Erdi̇nç, Alperen Kaan Bütüner, Hilal Erdoğan","doi":"10.47164/ijngc.v14i3.1287","DOIUrl":"https://doi.org/10.47164/ijngc.v14i3.1287","url":null,"abstract":"Plant pests cause significant economic losses to the agricultural sector. Tuta absoluta is one of the most important pests of the tomato plant, which has a high financial return. Accurate and rapid identification of tomato plant pests is essential to increase sustainable agricultural productivity. Computer vision and machine learning techniques such as deep learning and especially Convolutional Neural Networks (CNN) have effectively identified various plant pests. Training CNN-based algorithms that allow us to classify and identify objects can enable faster and more accurate pest detection than human observation. We used YOLOv5 (You Look Only Once), a CNN-based object detection algorithm. One thousand two hundred photos of tomato leaves infested by the T. absoluta pest were edited to train the YOLOv5 algorithm. Images include T. absoluta larvae and galleries created in leaves by these larvae. Experimental results showed that using the YOLOv5 algorithm could categorize tomato plant leaves correctly and detect T. absoluta larvae, galleries with 80% and 70-90% accuracy (mAP), respectively. The research is promising that deep learning-based object identification algorithms can be significantly effective in detecting agricultural pests early and preventing excessive use of pesticides.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"14 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139353503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Next-generation Digital Forensics Challenges and Evidence Preservation Framework for IoT Devices 物联网设备的下一代数字取证挑战和证据保存框架

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International Journal of Next-Generation Computing Pub Date : 2023-07-31 DOI: 10.47164/ijngc.v14i3.1078

Pankaj Sharma, L. Awasthi

{"title":"Next-generation Digital Forensics Challenges and Evidence Preservation Framework for IoT Devices","authors":"Pankaj Sharma, L. Awasthi","doi":"10.47164/ijngc.v14i3.1078","DOIUrl":"https://doi.org/10.47164/ijngc.v14i3.1078","url":null,"abstract":"The proliferation of the Internet of Things devices in today’s environment generates huge amount of information about users and surroundings. Data produced by IoT devices attracts cybercriminals to perform malicious activity. The technologies like cloud and fog computing are emerging as the next-generation infrastructure for Internet of Things which may be challenging for digital investigation. In this paper, IoT and fog-based frameworks for digital forensics of IoT devices are explained and tools used in different levels of IoT such as physical level, cloud level, network level, and mobile application level are briefly discussed. The process of evidence collection and challenges in IoT forensics paradigms are well studied. For securing the extracted artifacts IoT evidence preservation framework is proposed (IoT-EvPF). Furthermore, the forensic challenges in a cloud computing environment and anti-forensics techniques used by cybercriminals to hide their identity and malicious activity are discussed. We have identified research gaps and provided a framework to encourage more thought and conversation about the difficulties of retrieving digital evidence from Fog Computing systems.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"77 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139353468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Novel Deep Convolutional Neural Network based Classification of Arrhythmia 基于深度卷积神经网络的心律失常分类

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International Journal of Next-Generation Computing Pub Date : 2023-03-31 DOI: 10.47164/ijngc.v14i2.1153

Priyanka Rathee, Mahesh Shirsath, Lalit Kumar Awasthi, Naveen Chauhan

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A Mobile Sensing Based Stochastic Model to Forecast AQI Variation of Pollution Hotspots on Urban Neighborhoods 基于移动感知的城市住区污染热点地区空气质量指数随机预测模型

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International Journal of Next-Generation Computing Pub Date : 2023-03-31 DOI: 10.47164/ijngc.v14i2.1195

Ena Jain, Debopam Acharaya

{"title":"A Mobile Sensing Based Stochastic Model to Forecast AQI Variation of Pollution Hotspots on Urban Neighborhoods","authors":"Ena Jain, Debopam Acharaya","doi":"10.47164/ijngc.v14i2.1195","DOIUrl":"https://doi.org/10.47164/ijngc.v14i2.1195","url":null,"abstract":"Due to massive population migration, most Indian cities have experienced fast urbanization, resulting in a significant increase in construction activity, traffic pollution, and uncontrolled expansion. Some of these cities also have a high concentration of polluting industries, significantly worsening air quality. Pollution hotspots exist in certain cities, with levels well surpassing the authorized mark. Air pollution is generally classified as extremely hyper-local, which signifies that the pollution index decreases as we travel away from hotspots. Since the pollution data collected from traditional sources is occasionally inadequate, the extended consequences of such hotspots on neighboring communities remain unidentified. If the flux in pollution values in neighboring locales is efficiently mapped for locations encountered travelling further from identified hotspots, AQI levels for these areas can be forecasted and projected. Knowledge from monitoring these levels will aid the city administrations and government in drafting suitable proposals for susceptible establishments like hospitals and schools. In this research work, the Air Quality Index (AQI) data was accurately gathered at an identified pollution hotspot and its immediate neighborhood over a defined period along a specific route and a mathematical model was developed to forecast how AQI varies with distance for best results. Stochastic models such as ARMA and ARIMA were used to create the predicted model. Its reliability and performance were measured using various forecasting error calculation methods such as MPE (Mean Percentage Error), MAP (Mean Absolute Percentage), MAD (Mean Absolute Deviation), RMSE (Root Mean Square Error), and MSE (Mean Square Error).\u0000 ","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"47 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80134780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Security Issues, Attacks and Countermeasures in Layered IoT Ecosystem 分层物联网生态系统中的安全问题、攻击与对策

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International Journal of Next-Generation Computing Pub Date : 2023-03-31 DOI: 10.47164/ijngc.v14i2.892

Ajeet Singh, N D Patel

{"title":"Security Issues, Attacks and Countermeasures in Layered IoT Ecosystem","authors":"Ajeet Singh, N D Patel","doi":"10.47164/ijngc.v14i2.892","DOIUrl":"https://doi.org/10.47164/ijngc.v14i2.892","url":null,"abstract":"Internet of Things (IoT) applications consist mainly of a group of small devices with sensing and/ or actuationcapabilities, working collaboratively to provide a specific functionality. IoT applications are becoming vital part ofour daily lives in various areas such as home automation, industrial automation, energy sector, healthcare sectorand smart transportation. Security is a term that is used to encompass the notions such as integrity, confidentiality,and privacy. A more prominent understanding of the Internet of Things (IoT) is that – it transmits data over theglobal internet and gives many services in many domains. It facilitates the machines and gadgets to communicatewith each other. IoT appliances have been facing several issues, therefore we identify variety of service domainsand their vulnerabilities. The main focus is on protecting the security and privacy. This paper presents anoverview of IoT models, applications in different domains, vulnerabilities, security privacy goals, possible attacks,and their corresponding countermeasures. The objective of this paper is also to provide a survey on categorizedlayer-wise attacks and countermeasures in detail. In the object layer, connectivity link Layer, several attacksare discussed based on RFID, NFC, ZigBee, Bluetooth, and Wi-Fi protocols. In the Transport Network layer,we have classified variety of attacks based on RPL, 6loWPAN, TCP/UDP, and IPv4/IPv6. Similarly, In theSession Communication, Data Aggregation Storage, Business Model, and Application layers, we have discoveredconsiderable number of attacks for each layer.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"36 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84182215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Change Detection Analysis of Land Cover Features using Support Vector Machine Classifier 基于支持向量机分类器的土地覆盖特征变化检测分析

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International Journal of Next-Generation Computing Pub Date : 2023-03-31 DOI: 10.47164/ijngc.v14i2.384

Saurabh Kumar, Shwetank

{"title":"Change Detection Analysis of Land Cover Features using Support Vector Machine Classifier","authors":"Saurabh Kumar, Shwetank","doi":"10.47164/ijngc.v14i2.384","DOIUrl":"https://doi.org/10.47164/ijngc.v14i2.384","url":null,"abstract":"\u0000 \u0000 \u0000Remote sensing (RS) is crucial for geographical change studies such as vegetation, forestry, agriculture, urbanization, and other land use/land cover (LU/LC) applications. The RS satellite imagery provides crucial geospatial information for observation and analysis of the entire earth's surface. In the proposed study, Multitemporal and multispectral Landsat satellite imagery is used to feature extraction of LU/LC of the Haridwar region. The preprocessing of used imagery is essential for accurately classify the land cover features using image preprocessing methods (geometric correction, atmospheric correction, and image transform). It helps to classify and change detection of land cover features accurately. After preprocessing of imagery, land cover features are divided into seven feature classes using the region of interest (ROI) tool with google earth image and topographic map. The Support vector machine (SVM) is a supervised learning method used to classify the land cover features of the study area. SVM classifier accurately classifies the imagery of the different years 2017, 2010, 2003, and 1996 with 90.00%, 82.75%, 86.37%, and 83.38% accuracy. The post-classification method is used to detect changes in land cover features. From 1996 to 2017, orchards and vegetation are rapidly decreased by 13,698.36 ha and 1,638.81 ha due to unplanned development in urban and industrial areas of the Haridwar region. The resultant LU/LC change information is important for monitoring and analyzing land cover changes of the study area. \u0000 \u0000 \u0000 \u0000 \u0000 \u0000 \u0000 \u0000 \u0000","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"34 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82953363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0